1. Application of Cloud Analysis in GRAPES_RAFS Lijuan ZHU [1], Dehui CHEN [1], Zechun LI [1], Liping LIU [2], Zhifang XU [1], Ruixia LIU [3] [1] National Meteorological Centre (NMC) [2] Chinese Academy of Meteorological Sciences (CAMS) [3] National Satellite Meteorological Center (NSMC) China Meteorological Administration (CMA) ， Beijing, 100081 (25 October 2011, for workshop-NWP nowcasting in Boulder-USA)

2. Outline 1 Motivations 2 Cloud Analysis in GRAPES 3 Data used by C.A. of GRAPES 4 Preliminary results 5 Summary

3. 1 Motivations

4. There are a lot of data sets which are yet difficult to be directly assimilated, but could be fused for the model initialization for some reasons of technique approaches or computation effectiveness. These data sets are available, such as the satellite images or retrieved cloud products, surface visual + instrumental observations of cloud, visibility, lightning and so on, specially the radar reflectivity.

5. CMA’s Radar Network: CINRAD The observations of ~158 radars, which have been deployed in whole China (most along with East coast line), are available to be used.

6. 1 Motivations In other hand, a “cold-start” GRAPES is poor to provide the initial information of cloud for the microphysical scheme, and the associated moisture field and vertical motions. It is naturally motivated for us to fuse the available data sets for generating a more reasonable initial field with a detailed 3D cloud specification to produce the meso-scale cloud analysis products, and to improve short-time H.I.W. forecasts.

7. 2 Cloud Analysis in GRAPES

8. Cloud Analysis in GRAPES_RAFS （ 1 ） Cloud analysis scheme from ADAS of ARPS Model developed by CAPS,OU ( Xue et al., MAP, 2003 ; Hu, Xue et al., MWR, 2006 ) based on LAPS (Albers et al., 1996) Fusion of all cloud, precipitation observations Synop Satellite IR,VIS Radar Ref Background moisture Cloud field Cloud amount Cloud base Cloud thick Cloud type … … Hydrom. Backgroundobservations 3D cloud field, cloud amout Cloud type Cloud water, cloud ice Qc on cloud type (Cumulus) Precipitation type Precipitation (qr, qs, qh, …) Be nudged

9. dynamical relaxation factor And then the cloud analyzed information can be included by nudging method for the model initialization Cloud Analysis in GRAPES_RAFS （ 2 ） Cloud analysis can be called every 1 hour or every 3 hours.

10. Changes in the original C.A. (1) Correction in the code about Synop application to modify the background cloud base specification (barnes interpolation weights ): original modified

11. (2) The introduction of saturation on ice- surface scheme  Org: only water surface saturation  Modified by adding ice surface saturation

12. with ice surface saturation Org: water surface saturation only TRMM

13. (3) Permitting cloud water, cloud ice as well NCEP’s RUC: more suitable to stratus- cumulus (smaller upward motion in cloud), which dominate in most cases in China; Original scheme: more focused on deep convective cumulus (stronger upward motion in cloud) (4) Quality control of radar reflectivity Ground Clutter, Clear air echo, etc.

14. TRMM Cloud Water original modified

15. Cloud Ice TRMM originalmodified

16. 3 Data used by C.A. of GRAPES

17. Data used  Background: 3D grid fields of RH, Temperature, Pressure, surface temperature from 3DVAR analysis  SYNOP: Cloud base,Cloud amount  Radar 3D Mosaic Reflectivity Composite reflectivity over whole China or domain specified;

18. Satellite FY-2 IR TBBFY-2 VIS CTA SAT advantage: to specify the cloud top FY-2 Geostationary satellite, FY2D/2E ， every 30min ， but just hourly data used by RAFS Data use （ cont. ）

19. 4 Preliminary results

20. Specification of the experiment Case ： a Tropical Storm landed on Guangdong coast line Model: 15km GRAPES using T213 for 3DVAR FG and BC Background analysis: 3DVAR analysis downscaling to cloud analysis mesh of 5km as background of C.A. Initial Time ： Aug. 6, 2009 at 00UTC

21. b. cloudmodified c. base used IR TBBused radar reflect.used visible image Impact on cloud cover analysis IR TBB Obs.

22. Corrected the cloud base BeforeAfter

23. Cloud top compared to MODIS MODIS Cloud analysis

24. Cloud Type Radar Ref 1 St:Stratus 2 Sc:Stratocumulus 3 Cu:Cumulus 4 Ns:Nimbostratus 5 Ac:Altocumulus 6 AS:Altostratus 7 Cs:Cirrostratus 8 Ci:Cirrus 9 Cc:Cirrocumulus 10 Cb :Cumulonimbus

25. Compared to cloudsat cloudsat Cloud analysis Height(km)

26. Analyzed hydrometeors Radar reflectivity(Ob)Cloud water Cloud ice Qr Qs

27. Impact on forecast

28. 3h forecast Radar obs With cloud analysis Without cloud analysis With cloud analysis 6h forecast 12h forecast Radar obs Without cloud analysis With cloud analysis

29. All china<10mm<25mm<50mm<100mm Warm start0.3950.2030.0680.017 Warm start+cloud analysis 0.3980.2060.0660.033 TS-verification of 6H Precipitation forecasts (for July 5~30, 2009)

30. 5 Summary

31. Conclusion and discussion The cloud analysis scheme ADAS has been adapted to GRAPES_RAFS, and with some modifications. The preliminary experiments have showed the positive impacts. It still needs much further assessments. The quality control of the radar reflectivity is still a big challenge for real time application, not only due to the reflectivity quality itself, but also due to effectively receive the data in time.

32. Conclusion and discussion (cont.) The cloud analysis is a complicated issue. It is particularly necessary to adapt it according the stratus-cumulus which dominate in most cases in China. A lot of works are ongoing for real-time implementation of RAFS with C.A. at NMC/CMA.

33. Thanks!